Fig.REF  illustrates the performance of the mutual information estimators \(\hat{I}_{iDIME}\) , \(\hat{I}_{MINE}\)  and \(\hat{I}_{dDIME}\)  for three different values of the parameter \(\alpha \) . For both tasks with 2-d Gaussian samples and 10-d Gaussian samples, \(\hat{I}_{dDIME}\)  has slightly better accuracy than \(\hat{I}_{MINE}\)  and has much lower variance. The shadow colored regions show the variance of each estimator which has been evaluated given the optimal discriminator and given batches of size 512 taken from the empirical joint and marginal distributions, similarly to the analysis conducted in Sec.3 of [1]}. The dashed curves correspond to the final estimated mutual information. The parameter \(\alpha \)  seems to have an impact on the accuracy only for low SNRs, since at high SNRs, high values of \(\alpha \)  result in numerical issues.
\(\hat{I}_{iDIME}\)  performs better when dealing with higher dimensional data.
Fig.REF , instead, illustrates the performance of the mutual information estimators \(\hat{I}_{iDIME}\) , \(\hat{I}_{MINE}\)  and \(\tilde{I}_{dDIME}\)  for three different values of the parameter \(\alpha \) . For both tasks with 2-d Gaussian samples and 10-d Gaussian samples, \(\tilde{I}_{dDIME}\)  has a better accuracy than \(\hat{I}_{MINE}\)  but also than \(\hat{I}_{dDIME}\)  at the expense of a higher variance, similar to what happens with the variance of \(\hat{I}_{NWJ}\) , having an exponential behaviour as described in [2]}.
